Legal claims defining the scope of protection, as filed with the USPTO.
1. A method, comprising: in response to an occurrence of a failure in a storage controller, providing input on a plurality of attributes of the storage controller at a time of occurrence of the failure to a machine learning module; in response to receiving the input, generating, by the machine learning module, a plurality of output values corresponding to a plurality of recovery mechanisms to recover from the failure in the storage controller; and recovering from the failure in the storage controller, by applying a recovery mechanism whose output value is greatest among the plurality of output values that are generated by the machine learning module.
2. The method of claim 1 , wherein the storage controller controls access to a plurality of storage devices for a plurality of hosts, and wherein the storage controller is comprised of: a host adapter that is an interface between the storage controller and a host computational device; a device adapter that is an interface between the storage controller and a storage device that is in a Redundant Array of Independent Disks (RAID) configuration; a cache; and a non-volatile storage (NVS).
3. The method of claim 2 , wherein the plurality of attributes includes: measures corresponding to indications and characteristics of errors and panics that have been generated in the storage controller; and a measure of a hardware part associated with the failure.
4. The method of claim 2 , wherein the plurality of attributes includes: a measure of whether the cache is queued for segments; a measure of whether the NVS is queued for segments; a measure of whether the device adapter is queued for resources; and a measure of whether a RAID rebuild is in progress.
5. The method of claim 2 , wherein the plurality of attributes includes: a measure of whether the storage controller is executing a mainline code or an error recovery code at a time of the failure; a measure of whether the device adapter is fenced; and a measure of whether the host adapter is fenced.
6. The method of claim 2 , wherein the plurality of attributes includes: a measure of whether the storage controller is in a single server configuration or is in a dual server configuration; and a measure of previously known recovery mechanisms for errors corresponding to the failure.
7. The method of claim 1 , the method further comprising: transmitting, by the storage controller, the plurality of output values to a central computing device that generates weights and biases to be applied to machine learning modules of a plurality of storage controllers.
8. A system, comprising: a memory; and a processor coupled to the memory, wherein the processor performs operations, the operations comprising: in response to an occurrence of a failure in a storage controller, providing input on a plurality of attributes of the storage controller at a time of occurrence of the failure to a machine learning module; in response to receiving the input, generating, by the machine learning module, a plurality of output values corresponding to a plurality of recovery mechanisms to recover from the failure in the storage controller; and recovering from the failure in the storage controller, by applying a recovery mechanism whose output value is greatest among the plurality of output values that are generated by the machine learning module.
9. The system of claim 8 , wherein the storage controller controls access to a plurality of storage devices for a plurality of hosts, and wherein the storage controller is comprised of: a host adapter that is an interface between the storage controller and a host computational device; a device adapter that is an interface between the storage controller and a storage device that is in a Redundant Array of Independent Disks (RAID) configuration; a cache; and a non-volatile storage (NVS).
10. The system of claim 9 , wherein the plurality of attributes includes: measures corresponding to indications and characteristics of errors and panics that have been generated in the storage controller; and a measure of a hardware part associated with the failure.
11. The system of claim 9 , wherein the plurality of attributes includes: a measure of whether the cache is queued for segments; a measure of whether the NVS is queued for segments; a measure of whether the device adapter is queued for resources; and a measure of whether a RAID rebuild is in progress.
12. The system of claim 9 , wherein the plurality of attributes includes: a measure of whether the storage controller is executing a mainline code or an error recovery code at a time of the failure; a measure of whether the device adapter is fenced; and a measure of whether the host adapter is fenced.
13. The system of claim 9 , wherein the plurality of attributes includes: a measure of whether the storage controller is in a single server configuration or is in a dual server configuration; and a measure of previously known recovery mechanisms for errors corresponding to the failure.
14. The system of claim 8 , the operations further comprising: transmitting, by the storage controller, the plurality of output values to a central computing device that generates weights and biases to be applied to machine learning modules of a plurality of storage controllers.
15. A computer program product, the computer program product comprising a computer readable storage medium having computer readable program code embodied therewith, the computer readable program code configured to perform operations in a storage controller or a computational device, the operations comprising: in response to an occurrence of a failure in the storage controller, providing input on a plurality of attributes of the storage controller at a time of occurrence of the failure to a machine learning module; in response to receiving the input, generating, by the machine learning module, a plurality of output values corresponding to a plurality of recovery mechanisms to recover from the failure in the storage controller; and recovering from the failure in the storage controller, by applying a recovery mechanism whose output value is greatest among the plurality of output values that are generated by the machine learning module.
16. The computer program product of claim 15 , wherein the storage controller controls access to a plurality of storage devices for a plurality of hosts, and wherein the storage controller is comprised of: a host adapter that is an interface between the storage controller and a host computational device; a device adapter that is an interface between the storage controller and a storage device that is in a Redundant Array of Independent Disks (RAID) configuration; a cache; and a non-volatile storage (NVS).
17. The computer program product of claim 16 , wherein the plurality of attributes includes: measures corresponding to indications and characteristics of errors and panics that have been generated in the storage controller; and a measure of a hardware part associated with the failure.
18. The computer program product of claim 16 , wherein the plurality of attributes includes: a measure of whether the cache is queued for segments; a measure of whether the NVS is queued for segments; a measure of whether the device adapter is queued for resources; and a measure of whether a RAID rebuild is in progress.
19. The computer program product of claim 16 , wherein the plurality of attributes includes: a measure of whether the storage controller is executing a mainline code or an error recovery code at a time of the failure; a measure of whether the device adapter is fenced; a measure of whether the host adapter is fenced; a measure of whether the storage controller is in a single server configuration or is in a dual server configuration; and a measure of previously known recovery mechanisms for errors corresponding to the failure.
20. The computer program product of claim 15 , the operations further comprising: transmitting, by the storage controller, the plurality of output values to a central computing device that generates weights and biases to be applied to machine learning modules of a plurality of storage controllers.
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October 13, 2020
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